Autonomous, self-repairing explorers, such as deep space probes, have successfully performed complex missions by employing model-based executives that continuously monitor mission goals, diagnose failures and plan repairs. These executives employ models encoded as probabilistic constraint automata, in order to observe and control the hidden states of the system. These executives have also been incorporated within model-based programming languages that facilitate the creation of a wide range of fault adaptive systems, including automobiles and naval ships.
Future explorers, such as autonomous air vehicles and walking robots, will require far greater agility, in order to robustly achieve their missions. For example, to avoid falling, a walking robot must quickly detect a loss of balance, and replan its control trajectory appropriately. This talk presents recent advances in model-based programming and execution for agile systems. First, to reason about a system’s dynamics, these executives employ probabilistic constraint automata that are extended to hybrid discrete/continuous constraints. Second, to robustly achieve missions, these executives employ planning methods that reason about continuous, as well as discrete, state changes, and employ compilation and model-predictive control methods in order to adapt on the fly. Finally, these executives employ estimation methods for hybrid PHA that detect subtle failures through active control. Model-based execution is demonstrated both on a team of cooperative air vehicles and a biped walking machine.